#to double check if got use DT, scales, ggpubr
pacman::p_load(ggiraph, plotly, patchwork, DT, tidyverse,
knitr,scales,ggstatsplot,paletteer,wesanderson,
ggpubr,crosstalk,gganimate)
options(scipen = 999)Take Home Exercise 1
Visual Analytics of the demographic and financial characteristics of residents in City of Engagement
1. OVERVIEW
City of Engagement is a small city located at Country of Nowhere, with a total population of 50,000, serving as an agriculture region. The local council of the city is in the midst of preparing the Local Plan 2023.
1.1 The Task
In this take-home exercise, you are required to apply the concepts and methods you had learned in Lesson 1-4 to reveal the demographic and financial characteristics of the city of Engagement, using appropriate static and interactive statistical graphics methods.
2. Datasets
Data has been collected by the local council of the city. The survey sampled 1,000 respondents to collect data related to their household demographic, spending patterns and among, other things. The data is stored in two separate files ranging from Mar 2022 to Feb 2023 :
| 1) Participants.csv | 2) FinancialJourval.csv | |
|---|---|---|
| rows | 1,011 | 1,513,636 |
| variables | 7 | 4 |
2.1 Metadata
| File | Columns | Description |
|---|---|---|
| participants.csv | ||
3. Data Preparation
3.1 Install R-packages
Using p_load() of pacman package to load and install the following libraries:
ggiraph: For creating interactive ‘ggplot’ graphicsplotly: For creating interactive statistical graphspatchwork: For combining multiple ggplot2 graphs into one figuretidyverse: A collection of R packages use in everyday data analyses. It is able to support data science, data wrangling, and analysis.knitr: For dynamic report generationggstatsplot: For creating graphics with details from statistical tests included and its plotpaletteer: Collection of color paletteswesanderson: Wes Anderson’s theme Palette Generator
options(scipen = 999) : The above code removes scientific notation in our exercise.
3.2 Import Data
3.2.1 Import participants dataset
participants <- read_csv("data/Participants.csv")3.2.2 Load participants
# A tibble: 6 × 7
participantId householdSize haveKids age educationLevel interestGroup
<dbl> <dbl> <lgl> <dbl> <chr> <chr>
1 0 3 TRUE 36 HighSchoolOrCollege H
2 1 3 TRUE 25 HighSchoolOrCollege B
3 2 3 TRUE 35 HighSchoolOrCollege A
4 3 3 TRUE 21 HighSchoolOrCollege I
5 4 3 TRUE 43 Bachelors H
6 5 3 TRUE 32 HighSchoolOrCollege D
# ℹ 1 more variable: joviality <dbl>
head(participants)3.2.3 Import Financial Journal dataset
financial_journal <- read_csv("data/FinancialJournal.csv")3.2.4 Load Financial Journal
# A tibble: 6 × 4
participantId timestamp category amount
<dbl> <dttm> <chr> <dbl>
1 0 2022-03-01 00:00:00 Wage 2473.
2 0 2022-03-01 00:00:00 Shelter -555.
3 0 2022-03-01 00:00:00 Education -38.0
4 1 2022-03-01 00:00:00 Wage 2047.
5 1 2022-03-01 00:00:00 Shelter -555.
6 1 2022-03-01 00:00:00 Education -38.0
head(financial_journal)3.3 Data Wrangling
As seen from the two data tables above, there are several quality issues. 1) for Participants.csv
Inaccurate data types. - participantID is in dbl instead of chr -timestamp format is in POSIX instead of chr - householdSize is in dbl. We will convert it to ord. -reformat age group after looking at the min and max - round up jovality to 2 d.p.
Code with mutate from dplyr to reformat participantID from dbl to chr. Check for duplicates(remove 1,113 rows)
groupby participantID remove duplicates reformat timestamp to year and month create new variables for income, expenses, cashflow
The function distinct() [dplyr package] can be used to keep only unique/distinct rows from a data frame.
unique(financial_journal$category)[1] "Wage" "Shelter" "Education" "RentAdjustment"
[5] "Food" "Recreation"
Show the code
#create new dataset
participants_new <- participants %>%
mutate(
participantId = as.character(participantId),
#binned joviality to 5-class variables
joviality_bins = cut(joviality, breaks = c(0.0,0.2,0.4,0.6,0.8,1.0))
)
#reformat householdSize to Ordinal
participants_new$householdSize <- factor(participants$householdSize,
levels = c("1", "2", "3"),
ordered = TRUE)
#reformat age group
participants_new$age_group <- factor(ifelse(participants$age < 20, "Under 20",
ifelse(participants$age < 30, "20-29",
ifelse(participants$age < 40, "30-39",
ifelse(participants$age < 50, "40-49", "Above 50")))),
levels = c("Under 20", "20-29", "30-39", "40-49", "Above 50"),
ordered= TRUE)
#reformat education level to Ordinal
participants_new$educationLevel <- factor(participants$educationLevel,
levels = c("Low", "HighSchoolOrCollege",
"Bachelors", "Graduate"
),
ordered = TRUE)
#round up joviality to 2 decimal places
participants_new$joviality <- round(participants$joviality, 2)
participants_new# A tibble: 1,011 × 9
participantId householdSize haveKids age educationLevel interestGroup
<chr> <ord> <lgl> <dbl> <ord> <chr>
1 0 3 TRUE 36 HighSchoolOrCollege H
2 1 3 TRUE 25 HighSchoolOrCollege B
3 2 3 TRUE 35 HighSchoolOrCollege A
4 3 3 TRUE 21 HighSchoolOrCollege I
5 4 3 TRUE 43 Bachelors H
6 5 3 TRUE 32 HighSchoolOrCollege D
7 6 3 TRUE 26 HighSchoolOrCollege I
8 7 3 TRUE 27 Bachelors A
9 8 3 TRUE 20 Bachelors G
10 9 3 TRUE 35 Bachelors D
# ℹ 1,001 more rows
# ℹ 3 more variables: joviality <dbl>, joviality_bins <fct>, age_group <ord>
#check min and max age of residents in COE.
min(participants$age)[1] 18
max(participants$age)[1] 60
#remove duplicate rows for all columns
financial_journal_lessdup <- financial_journal %>%
distinct()You can use group_by() function along with the summarise() from dplyr package to find the group by sum in R DataFrame, group_by() returns the grouped_df ( A grouped Data Frame) and use summarise() on grouped df results to get the group by sum.
scales package (part of the Tidyverse) does exactly this:
Show the code
#create new dataset
grouped_data <- financial_journal_lessdup %>%
#recode ID from dbl to chr, year_mth
mutate(participantId = as.character(participantId),
year_mth = format(as.Date(financial_journal_lessdup$timestamp), "%Y-%m"),
amount = abs(round(amount,2)),
.before = 3) %>%
#group the columns in the following order
group_by(participantId,year_mth, category) %>%
summarize(total_amount = sum(amount))
# Pivot the data frame to have categories as columns
pivoted_fj <- grouped_data %>%
pivot_wider(names_from = "category", values_from = "total_amount", values_fill = 0)
# Add a new column with mixed categories
pivoted_fj$Expenses <- pivoted_fj$Education + pivoted_fj$Food + pivoted_fj$Recreation + pivoted_fj$Shelter +pivoted_fj$RentAdjustment
pivoted_fj$Income <- pivoted_fj$Wage
pivoted_fj$Cashflow <- pivoted_fj$Income - pivoted_fj$Expenses
pivoted_fj$Shelter <- pivoted_fj$Shelter + pivoted_fj$RentAdjustment
# Output the pivoted data frame
pivoted_fj# A tibble: 10,691 × 11
# Groups: participantId, year_mth [10,691]
participantId year_mth Education Food Recreation Shelter Wage
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 2022-03 38.0 268. 349. 555. 11932.
2 0 2022-04 38.0 266. 219. 555. 8637.
3 0 2022-05 38.0 265. 383. 555. 9048.
4 0 2022-06 38.0 257. 466. 555. 9048.
5 0 2022-07 38.0 270. 1069. 555. 8637.
6 0 2022-08 38.0 262. 314. 555. 9459.
7 0 2022-09 38.0 256. 295. 555. 9048.
8 0 2022-10 38.0 267. 25.0 555. 8637.
9 0 2022-11 38.0 261 377. 555. 9048.
10 0 2022-12 38.0 266. 357. 555. 9048.
# ℹ 10,681 more rows
# ℹ 4 more variables: RentAdjustment <dbl>, Expenses <dbl>, Income <dbl>,
# Cashflow <dbl>
The function distinct() [dplyr package] can be used to keep only unique/distinct rows from a data frame. If there are duplicate rows, only the first row is preserved.
colSums(pivoted_fj[-1] !=0) year_mth Education Food Recreation Shelter
10691 3018 10691 9492 10560
Wage RentAdjustment Expenses Income Cashflow
10691 72 10691 10691 10691
Check for missing values
#Check for missing values
any(is.na(participants_new))[1] FALSE
any(is.na(pivoted_fj))[1] FALSE
Merge Data Table
Show the code
#join both data sets
resident_profile <- full_join(participants_new, pivoted_fj,
by = c("participantId" = "participantId")) %>%
#relocate columns to the front (by importance)
relocate(year_mth, .after =participantId) %>%
relocate(Cashflow, .after = year_mth) %>%
relocate(age_group, .after = Cashflow) %>%
relocate(educationLevel, .after = age_group) %>%
relocate(Income, .after = haveKids) %>%
relocate(Expenses , .after = Income)
resident_profile %>%
select(c(1:18))# A tibble: 10,691 × 18
participantId year_mth Cashflow age_group educationLevel householdSize
<chr> <chr> <dbl> <ord> <ord> <ord>
1 0 2022-03 10722. 30-39 HighSchoolOrCollege 3
2 0 2022-04 7559. 30-39 HighSchoolOrCollege 3
3 0 2022-05 7808. 30-39 HighSchoolOrCollege 3
4 0 2022-06 7733. 30-39 HighSchoolOrCollege 3
5 0 2022-07 6704. 30-39 HighSchoolOrCollege 3
6 0 2022-08 8291. 30-39 HighSchoolOrCollege 3
7 0 2022-09 7904. 30-39 HighSchoolOrCollege 3
8 0 2022-10 7752. 30-39 HighSchoolOrCollege 3
9 0 2022-11 7817. 30-39 HighSchoolOrCollege 3
10 0 2022-12 7832. 30-39 HighSchoolOrCollege 3
# ℹ 10,681 more rows
# ℹ 12 more variables: haveKids <lgl>, Income <dbl>, Expenses <dbl>, age <dbl>,
# interestGroup <chr>, joviality <dbl>, joviality_bins <fct>,
# Education <dbl>, Food <dbl>, Recreation <dbl>, Shelter <dbl>, Wage <dbl>
kable(head(resident_profile), "simple")| participantId | year_mth | Cashflow | age_group | educationLevel | householdSize | haveKids | Income | Expenses | age | interestGroup | joviality | joviality_bins | Education | Food | Recreation | Shelter | Wage | RentAdjustment |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2022-03 | 10722.01 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 11931.95 | 1209.94 | 36 | H | 0 | (0,0.2] | 38.01 | 268.26 | 348.68 | 554.99 | 11931.95 | 0 |
| 0 | 2022-04 | 7558.67 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 8636.88 | 1078.21 | 36 | H | 0 | (0,0.2] | 38.01 | 265.79 | 219.42 | 554.99 | 8636.88 | 0 |
| 0 | 2022-05 | 7807.63 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 9048.16 | 1240.53 | 36 | H | 0 | (0,0.2] | 38.01 | 264.54 | 382.99 | 554.99 | 9048.16 | 0 |
| 0 | 2022-06 | 7732.59 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 9048.16 | 1315.57 | 36 | H | 0 | (0,0.2] | 38.01 | 256.90 | 465.67 | 554.99 | 9048.16 | 0 |
| 0 | 2022-07 | 6704.27 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 8636.88 | 1932.61 | 36 | H | 0 | (0,0.2] | 38.01 | 270.13 | 1069.48 | 554.99 | 8636.88 | 0 |
| 0 | 2022-08 | 8290.55 | 30-39 | HighSchoolOrCollege | 3 | TRUE | 9459.44 | 1168.89 | 36 | H | 0 | (0,0.2] | 38.01 | 261.76 | 314.13 | 554.99 | 9459.44 | 0 |
4. Exploratory Data Analysis (EDA)
Overview of City of Engagement’s resident.
Show the code
#create tooltip to display age group
participants_new$tooltip <-c(paste0(
"Age Group:", participants_new$age_group))
#Bar chart for resident's age distribution
p1 <- ggplot(data= participants_new,
aes(x = age_group)) +
geom_bar_interactive(aes(tooltip = participants_new$tooltip, stackgroups = TRUE,
data_id= age_group)) +
scale_fill_manual(values = wes_palette("Chevalier1")) +
xlab("Age Group") +
ylab("No.of\nResidents") +
theme(axis.text.x=element_text(size=5)) +
theme(axis.title.y=element_text(size=10)) +
ylim(0,300)
#Bar chart for resident's household size distribution
p2 <- ggplot(data= participants_new,
aes(x = householdSize)) +
geom_bar_interactive(aes(tooltip = participants_new$tooltip, stackgroups = TRUE,
data_id= age_group)) +
scale_fill_manual(values = wes_palette("Chevalier1")) +
xlab("Size of Household") +
ylab("No.of Residents") +
theme(axis.text.x=element_text(size=5)) +
theme(axis.title.y=element_text(size=10)) +
ylim(0,400)
#Bar chart for resident's education level
p3 <- ggplot(data= participants_new,
aes(x = educationLevel)) +
geom_bar_interactive(aes(tooltip = participants_new$tooltip, stackgroups = TRUE,
data_id= age_group)) +
xlab("Education Level") +
ylab("No.of\nResidents") +
theme(axis.text.x=element_text(size=5)) +
theme(axis.title.y=element_text(size=10)) +
ylim(0,600)
#Bar chart to visualize if residents have kids
p4 <- ggplot(data= participants_new,
aes(x = haveKids)) +
geom_bar_interactive(aes(tooltip = participants_new$tooltip, stackgroups = TRUE,
data_id= age_group)) +
ylab("No.of\nResidents") +
theme(axis.text.x=element_text(size=5)) +
theme(axis.title.y=element_text(size=10)) +
ylim(0,850)
#Bar chart for residents' interest group
p5 <- ggplot(data= participants_new,
aes(x = interestGroup)) +
geom_bar_interactive(aes(tooltip = participants_new$tooltip, stackgroups = TRUE,
data_id= age_group)) +
xlab("Interest Group") +
ylab("No.of\nResidents") +
theme(axis.text.x=element_text(size=5)) +
theme(axis.title.y=element_text(size=10)) +
ylim(0,150)
#figure design layout
design <- "
132
132
132
554
"
girafe(code = print(p1 + p2 + p3 + p4 + p5 +
plot_layout(design = design, ) +
plot_annotation(title =
"Demographics Insights of residents \n in City of Engagement",
theme = theme(plot.title = element_text(size = 20, hjust=0.5))
)),
width_svg = 12,
height_svg = 6,
options = list(
opts_hover(css = "fill: #02401B;"),
opts_hover_inv(css = "opacity:0.2;")
)
) Insights:
Click on the graph and hover around each demographics. The respective age group will be displayed.
4.1 Chi-square test for Age group and Education Level
ggbarstats(data = resident_profile,
x = educationLevel, y = age_group,
package = "wesanderson", palette = "Chevalier1"
)
4.2 Differences in Joviality based on Education Level
As defined, Joviality indicates the participant’s overall happiness at the start of the study. We will like to found out if there is a difference in Joviality based on Education Level. Before testing our hypothesis, we will perform a normality assumption test at 95% confidence level.
4.2.1 Normality Assumption Test
At 95% confidence level:
Ho: the mean Joviality across different Education Level is the same
H1: the mean Joviality across different Education Level is not the same
#compute statistics
average_joviality <- round(mean(resident_profile$joviality),2)
median_joviality <- round(median(resident_profile$joviality),2)
maximum_joviality <- as.numeric(round((IQR(resident_profile$joviality)*1.5) +
quantile(resident_profile$joviality,0.75)),2)
min_joviality <- as.integer(min(resident_profile$joviality))
IQR_joviality <- round(IQR(resident_profile$joviality)*1.5)
average_joviality[1] 0.47
median_joviality[1] 0.45
maximum_joviality[1] 1
min_joviality[1] 0
IQR_joviality[1] 1
ggplot(data = resident_profile ,
aes(x=joviality, after_stat(count), color = educationLevel)) +
geom_density(adjust=1, position ="stack") +
xlim(0,1) +
geom_vline(aes(xintercept = average_joviality), col ="grey", linewidth=1 ) +
geom_vline(aes(xintercept = median_joviality), col ="orange", linewidth=1 ) 
ggplot(data=resident_profile,
aes(sample = joviality)) +
stat_qq() +
stat_qq_line()
ggplot(data = resident_profile,
aes(x= joviality)) +
geom_histogram(bins=10) +
xlab("Joviality") +
ylab("No.of Residents") +
geom_vline(aes(xintercept = average_joviality), col ="red", linewidth=1 ) +
annotate("text", x=0.37, y= 1400, label="Average Joviality:", size=4, color = "red") + annotate("text", x=0.37, y= 1300, label=format(average_joviality, big.mark=","), size=4, color = "red") 
geom_vline(aes(xintercept = median_joviality), col ="green", linewidth=1 ) mapping: xintercept = ~median_joviality
geom_vline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
Based on the result above, we concluded that there is enough statistical evidence to reject the null hypothesis. Since the p-values fall below (p < 0.05), we will use the Wilcoxon test.
4.2.2 Kruskal-Wallis Test for Joviality across Education Level
We will test the following hypothesis at 95% Confidence Level:
Ho : the median Joviality across different education level is the same
H1: the median Joviality across different education level is not the same
Show the code
ggbetweenstats(data = resident_profile,
x= educationLevel, y= joviality, type ="np",
xlab= "Education Level", ylab = "Joviality",
title = "Comparison of Joviality across Education Level",
pairwise.comparisons = TRUE, pairwise.display ="ns", conf.level = 0.95,
package = "wesanderson", palette = "Chevalier1"
)
As seen above, the P-value is lower than the 0.05. As such, there is enough statistical evidence to reject the null hypothesis that the median joviality across education level is the same.
Additionally, we want to find out if there any distinct similarities between the district. Through the graph above, we discovered that not all pair comparison are statistically significant. The pair (Low and HighSchoolOrCollege) is not statistically significant with a P-value of 0.14, which is greater than 0.05. Thus, we cannot reject the null hypothesis that there is not differences between the joviality level between the pair.
4.3 Association between Age group and Joviality
We discovered that there is a statistical difference in joviality across Education Level, we would like to examine if the same applies and if there is an association between the age group. As Joviality is a continuous variable, we used mutate from dplyr to split it into 5-class variables, with each range at 20%.
At 95% confidence level,
Ho : No association exists between the age group and joviality level
H1: Association exists between the age group and joviality level
ggbarstats(data = resident_profile,
x = joviality_bins, y = age_group,
title = "Comparison of Joviality across age-group",
pairwise.comparisons = TRUE, pairwise.display ="s", conf.level = 0.95,
package = "wesanderson", palette = "Chevalier1"
)
##KIV TO COME BACK AND EDIT.
p3 <- ggplot(data= resident_profile,
aes(x = Income)) +
geom_histogram(bins=30,
color="black",
fill= "darkgrey") +
xlab("Income") +
ylab("No.of\nResidents") +
ylim(0,2100) +
xlim(-1000,20000) +
ggtitle("Income Distribution of Residents ") +
theme(plot.title = element_text(hjust = 0.5))
p3
5. Financial Health Insights
to include graphs of ID financial health over the 2 years.
5.1 Resident’s Financial Health
Show the code
tooltip_css <- "background-color:lightgrey; #<<
font-style:bold; color:black;" #<<
ie <- ggplot(data=resident_profile) +
geom_point_interactive (aes(x=Expenses, y= Income,
tooltip = participantId, data_id = Cashflow)) +
xlim(0,5000)
girafe(
ggobj = ie,
width_svg = 6,
height_svg = 6*0.618,
options = list( #<<
opts_tooltip(css = tooltip_css), #<<
opts_hover_inv(css = "opacity:0.2;") #<<
)
) 7 rows have been excluded in the graph
5.2 Differences in joviality across income
We want to find out if there are any differences in joviality across income.
The following considerations have been made:
assume income increases over the years
opacity included to highlight the contrast
Tooltip to indicate variables
participantID
Income
Cashflow
We will be using plot_ly for the interactive graph.
plot_ly(data = resident_profile,
x = ~joviality, y = ~Income,
hovertemplate = ~paste("<br>Participant's ID:",participantId,
"<br>Cashflow:", Cashflow,
"<br>Income:", Income,
"<br>Expenses:", Expenses),
type = "scatter",
mode = "markers",
marker = list(opacity = 0.7,sizemode = "diameter",
line = list(width =0.1, color = "white"))) |>
#add title and labels to axis
layout(title = "Interactive scatterplot of Income vs Joviality" ,
xaxis = list(title = "Joviality level") ,
yaxis = list(title = "Income"))Insights
When income exceeds $15k, joviality level decreases
it is similar to income range of $10-$15k where majority have low joviality
Joviality level are more spread out when income range below $5k
Happiness level of joviality ranges from 0 to 1. A score of 0.4 indicates that participant is dull while a score of 0.8 indicates joy.
DT::datatable(resident_profile, class= "compact")d <- highlight_key(resident_profile)
p <- ggplot(d,
aes(Expenses,
Income)) +
geom_point(size =1)
gg <- highlight(ggplotly(p),
"plotly_selected")
dt <- DT::datatable(d,
selection =list(mode="single",
target="column"))
crosstalk::bscols(gg,
dt,
widths = 6) fj_cat <- financial_journal_lessdup %>%
#recode ID from dbl to chr, year_mth
mutate(participantId = as.character(participantId),
year = format(as.Date(financial_journal_lessdup$timestamp), "%Y"),
mth = format(as.Date(financial_journal_lessdup$timestamp), "%m"),
amount = abs(round(amount,2)),
.before = 3) %>%
#group the columns in the following order
group_by(year, mth, category) %>%
summarize(total_amount = sum(amount))
fj_cat# A tibble: 62 × 4
# Groups: year, mth [12]
year mth category total_amount
<chr> <chr> <chr> <dbl>
1 2022 03 Education 14354.
2 2022 03 Food 327829.
3 2022 03 Recreation 649580.
4 2022 03 RentAdjustment 53504.
5 2022 03 Shelter 631623.
6 2022 03 Wage 6402720.
7 2022 04 Education 11424.
8 2022 04 Food 304282.
9 2022 04 Recreation 389688.
10 2022 04 RentAdjustment 1429.
# ℹ 52 more rows
ggplot(data = fj_cat,
aes(x= category, y=total_amount, size = total_amount)) +
geom_point(alpha=0.8, show.legend= FALSE) +
scale_size(range =c(2,12)) +
labs(titles = "Year{as.integer(frame_time)}",
x = "Category",
y = "Total_amount") +
transition_time(as.integer(year)) +
ease_aes("linear") +
scale_y_continuous(limit =c(0,2000000))
Average Cashflow available for Residents across Age Group
Show the code
tooltip_css <- "background-color:#C7B19C; #<<
font-style:bold; color:#446455;" #<<
tooltip <- function(y, ymax, accuracy = 1) { #<<
mean <- scales::number(y, accuracy = accuracy) #<<
sem <- scales::number(ymax - y, accuracy = accuracy) #<<
paste("Average Cashflow:", mean, "+/-", sem) #<<
} #<<
pp <- ggplot(data=resident_profile,
aes(x = age_group),
) +
stat_summary(aes(y = Cashflow,
tooltip = after_stat( #<<
tooltip(y, ymax))), #<<
fun.data = "mean_se",
geom = GeomInteractiveCol, #<<
fill = "#D3DDDC"
) +
stat_summary(aes(y = Cashflow),
fun.data = mean_se,
geom = "errorbar", width = 0.2, linewidth = 0.2
) +
labs(title="Average Cashflow available to Residents by age_group") +
ylab("Total Cashflow") +
xlab("Age Group")
girafe(ggobj = pp,
width_svg = 8,
height_svg = 8*0.618,
options = list( #<<
opts_tooltip( #<<
css = tooltip_css)) #<<
)